Machine learning under malware attack:
Saved in:
| Main Author: | |
|---|---|
| Format: | Thesis Book |
| Language: | English |
| Published: |
Wiesbaden
Springer Vieweg
[2023]
|
| Series: | Research
|
| Subjects: | |
| Online Access: | Inhaltstext Inhaltsverzeichnis Inhaltsverzeichnis |
| Physical Description: | xxxiv, 116 Seiten Illustrationen, Diagramme 21 cm x 14.8 cm, 209 g |
| ISBN: | 9783658404413 3658404418 |
Staff View
MARC
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| 041 | 0 | |a eng | |
| 044 | |a gw |c XA-DE-HE | ||
| 049 | |a DE-706 |a DE-83 | ||
| 084 | |a ST 300 |0 (DE-625)143650: |2 rvk | ||
| 084 | |8 1\p |a 004 |2 23sdnb | ||
| 100 | 1 | |a Labaca-Castro, Raphael |e Verfasser |0 (DE-588)1290186871 |4 aut | |
| 245 | 1 | 0 | |a Machine learning under malware attack |c Raphael Labaca-Castro |
| 264 | 1 | |a Wiesbaden |b Springer Vieweg |c [2023] | |
| 264 | 4 | |c © 2023 | |
| 300 | |a xxxiv, 116 Seiten |b Illustrationen, Diagramme |c 21 cm x 14.8 cm, 209 g | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 490 | 0 | |a Research | |
| 502 | |b Dissertation |c Universität der Bundeswehr München |d 2022 | ||
| 650 | 0 | 7 | |a Maschinelles Lernen |0 (DE-588)4193754-5 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Computersicherheit |0 (DE-588)4274324-2 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Malware |0 (DE-588)4687059-3 |2 gnd |9 rswk-swf |
| 653 | |a Computer Security | ||
| 653 | |a Machine Learning | ||
| 653 | |a Adversarial ML | ||
| 653 | |a Trustworthy AI | ||
| 653 | |a Malware | ||
| 653 | |a FAME | ||
| 655 | 7 | |0 (DE-588)4113937-9 |a Hochschulschrift |2 gnd-content | |
| 689 | 0 | 0 | |a Maschinelles Lernen |0 (DE-588)4193754-5 |D s |
| 689 | 0 | 1 | |a Malware |0 (DE-588)4687059-3 |D s |
| 689 | 0 | 2 | |a Computersicherheit |0 (DE-588)4274324-2 |D s |
| 689 | 0 | |5 DE-604 | |
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Record in the Search Index
| _version_ | 1804185205460697088 |
|---|---|
| adam_text | CONTENTS
PART
I
THE
BEGINNINGS
OF
ADVERSARIAL
ML
1
INTRODUCTION
....................................................................................................
3
1.1
MOTIVATION
......................................................................................
3
1.2
PROBLEM
STATEMENT
........................................................................
4
1.3
ORGANIZATION
OF
DISSERTATION
.........................................................
7
2
BACKGROUND
....................................................................................................
9
2.1
MALWARE
.........................................................................................
9
2.2
PORTABLE
EXECUTABLE
FORMAT
........................................................
10
2.3
RELATED
WORK
................................................................................
12
2.3.1
GRADIENT
OPTIMIZATION
...................................................
13
2.3.2
GENERATIVE
ADVERSARIAL
NETWORKS
..................................
14
2.3.3
REINFORCEMENT
LEARNING
.................................................
15
2.3.4
GENETIC
PROGRAMMING
.....................................................
16
2.3.5
UNIVERSAL
ADVERSARIAL
PERTURBATIONS
..............................
18
2.4
SUMMARY
.......................................................................................
19
PART
II
FRAMEWORK
FOR
ADVERSARIAL
MALWARE
EVALUATION
3
FAME
..............................................................................................................
23
3.1
NOTATION
.........................................................................................
23
3.1.1
FEATURE-SPACE
ATTACKS
...................................................
24
3.1.2
FEATURE-SPACE
CONSTRAINTS
..............................................
24
3.1.3
PROBLEM-SPACE
ATTACKS
.................................................
24
3.1.4
PROBLEM-SPACE
CONSTRAINTS
............................................
24
3.2
THREAT
MODEL
...............................................................................
25
XXIV
CONTENTS
3.2.1
ADVERSARY
OBJECTIVES
.....................................................
25
3.2.2
ADVERSARY
KNOWLEDGE
...................................................
25
3.2.3
ADVERSARY
CAPABILITIES
...................................................
26
3.3
EXPERIMENTAL
SETTINGS
..................................................................
26
3.3.1
TARGET
MODEL
...................................................................
27
3.3.2
DATASETS
............................................................................
27
3.3.3
BYTE-LEVEL
PERTURBATIONS
...............................................
29
3.3.4
INTEGRITY
VERIFICATION
.......................................................
30
3.4
SUMMARY
........................................................................................
31
PART
III
PROBLEM-SPACE
ATTACKS
4
STOCHASTIC
METHOD
.........................................................................................
35
4.1
REQUIREMENTS
.................................................................................
36
4.2
METHODOLOGY
...................................................................................
36
4.2.1
TOOLBOX
............................................................................
37
4.2.2
VERIFIER
............................................................................
37
4.2.3
CLASSIFIER
..........................................................................
37
4.3
EVALUATION
.......................................................................................
38
4.3.1
INTEGRITY
..........................................................................
38
4.3.2
EVASIVENESS
.....................................................................
40
4.3.3
HIGH-DIMENSIONAL
SEQUENCES
.......................................
41
4.4
SUMMARY
.........................................................................................
42
5
GENETIC
PROGRAMMING
..................................................................................
43
5.1
REQUIREMENTS
.................................................................................
44
5.2
METHODOLOGY
...................................................................................
45
5.2.1
GENETIC
OPERATORS
..........................................................
46
5.2.2
FITNESS
FUNCTION
............................................................
47
5.3
EVALUATION
.......................................................................................
47
5.4
CROSS-EVASION
.................................................................................
49
5.5
SUMMARY
.........................................................................................
50
6
REINFORCEMENT
LEARNING
..............................................................................
51
6.1
METHODOLOGY
...................................................................................
52
6.1.1
STATE
..................................................................................
53
6.1.2
ACTION
..............................................................................
53
6.1.3
REWARD
.............................................................................
53
6.1.4
PENALTY
............................................................................
55
6.1.5
MODEL
PARAMETERS
...........................................................
55
CONTENTS
XXV
6.2
EVALUATION
.....................................................................................
56
6.2.1
RESET
................................................................................
56
6.2.2
DIVERSITY
..........................................................................
56
6.2.3
EVASION
RATE
...................................................................
57
6.3
SUMMARY
........................................................................................
60
7
UNIVERSAL
ATTACKS
.........................................................................................
61
7.1
REQUIREMENTS
................................................................................
62
7.2
UNIVERSAL
EVASION
RATE
.................................................................
62
7.3
UAP
SEARCH
....................................................................................
62
7.4
EVALUATION
......................................................................................
64
7.5
SUMMARY
........................................................................................
65
PART
IV
FEATURE-SPACE
ATTACKS
8
GRADIENT
OPTIMIZATION
...............................................................................
69
8.1
CONVOLUTIONAL
NEURAL
NETWORKS
.....................................................
69
8.2
METHODOLOGY
...............................................................................
70
8.3
SUMMARY
........................................................................................
72
9
GENERATIVE
ADVERSARIAL
NETS
.....................................................................
73
9.1
GANS
IN
SECURITY
...........................................................................
74
9.2
METHODOLOGY
...............................................................................
74
9.3
SUMMARY
.........................................................................................
76
PART
V
BENCHMARK
&
DEFENSES
10
COMPARISON
OF
STRATEGIES
...........................................................................
79
10.1
BENCHMARK
SETTINGS
......................................................................
79
10.2
SUMMARY
........................................................................................
81
11
TOWARDS
ROBUSTNESS
.....................................................................................
83
11.1
FEATURE
REDUCTION
........................................................................
84
11.2
UAP-BASED
ADVERSARIAL
TRAINING
...............................................
85
11.2.1
EVALUATE
HARDENED
MODELS
...........................................
87
11.2.2
UAP
SEARCH
ALTERNATIVE
................................................
89
11.3
SUMMARY
........................................................................................
91
XXVI
CONTENTS
PART
VI
CLOSING
REMARKS
12
CONCLUSIONS
&
OUTLOOK
................................................................................
95
12.1
REVISING
RESEARCH
QUESTIONS
.......................................................
96
12.2
CONTRIBUTIONS
..................................................................................
99
12.2.1
EMPIRICAL
GUIDANCE
FOR
MALWARE
ATTACKS
..................
99
12.2.2
ADVERSARIAL
DEFENSES
DERIVED
FROM
UAP
ATTACKS
....
100
12.2.3
FRAMEWORK
TO
EVALUATE
MALWARE
CLASSIFIERS
..............
100
12.3
CONNECTING
THE
DOTS
......................................................................
101
12.4
FUTURE
WORK
..................................................................................
102
LIST
OF
PUBLICATIONS
................................................................................................
105
REFERENCES
................................................................................................................
107
|
| adam_txt |
CONTENTS
PART
I
THE
BEGINNINGS
OF
ADVERSARIAL
ML
1
INTRODUCTION
.
3
1.1
MOTIVATION
.
3
1.2
PROBLEM
STATEMENT
.
4
1.3
ORGANIZATION
OF
DISSERTATION
.
7
2
BACKGROUND
.
9
2.1
MALWARE
.
9
2.2
PORTABLE
EXECUTABLE
FORMAT
.
10
2.3
RELATED
WORK
.
12
2.3.1
GRADIENT
OPTIMIZATION
.
13
2.3.2
GENERATIVE
ADVERSARIAL
NETWORKS
.
14
2.3.3
REINFORCEMENT
LEARNING
.
15
2.3.4
GENETIC
PROGRAMMING
.
16
2.3.5
UNIVERSAL
ADVERSARIAL
PERTURBATIONS
.
18
2.4
SUMMARY
.
19
PART
II
FRAMEWORK
FOR
ADVERSARIAL
MALWARE
EVALUATION
3
FAME
.
23
3.1
NOTATION
.
23
3.1.1
FEATURE-SPACE
ATTACKS
.
24
3.1.2
FEATURE-SPACE
CONSTRAINTS
.
24
3.1.3
PROBLEM-SPACE
ATTACKS
.
24
3.1.4
PROBLEM-SPACE
CONSTRAINTS
.
24
3.2
THREAT
MODEL
.
25
XXIV
CONTENTS
3.2.1
ADVERSARY
OBJECTIVES
.
25
3.2.2
ADVERSARY
KNOWLEDGE
.
25
3.2.3
ADVERSARY
CAPABILITIES
.
26
3.3
EXPERIMENTAL
SETTINGS
.
26
3.3.1
TARGET
MODEL
.
27
3.3.2
DATASETS
.
27
3.3.3
BYTE-LEVEL
PERTURBATIONS
.
29
3.3.4
INTEGRITY
VERIFICATION
.
30
3.4
SUMMARY
.
31
PART
III
PROBLEM-SPACE
ATTACKS
4
STOCHASTIC
METHOD
.
35
4.1
REQUIREMENTS
.
36
4.2
METHODOLOGY
.
36
4.2.1
TOOLBOX
.
37
4.2.2
VERIFIER
.
37
4.2.3
CLASSIFIER
.
37
4.3
EVALUATION
.
38
4.3.1
INTEGRITY
.
38
4.3.2
EVASIVENESS
.
40
4.3.3
HIGH-DIMENSIONAL
SEQUENCES
.
41
4.4
SUMMARY
.
42
5
GENETIC
PROGRAMMING
.
43
5.1
REQUIREMENTS
.
44
5.2
METHODOLOGY
.
45
5.2.1
GENETIC
OPERATORS
.
46
5.2.2
FITNESS
FUNCTION
.
47
5.3
EVALUATION
.
47
5.4
CROSS-EVASION
.
49
5.5
SUMMARY
.
50
6
REINFORCEMENT
LEARNING
.
51
6.1
METHODOLOGY
.
52
6.1.1
STATE
.
53
6.1.2
ACTION
.
53
6.1.3
REWARD
.
53
6.1.4
PENALTY
.
55
6.1.5
MODEL
PARAMETERS
.
55
CONTENTS
XXV
6.2
EVALUATION
.
56
6.2.1
RESET
.
56
6.2.2
DIVERSITY
.
56
6.2.3
EVASION
RATE
.
57
6.3
SUMMARY
.
60
7
UNIVERSAL
ATTACKS
.
61
7.1
REQUIREMENTS
.
62
7.2
UNIVERSAL
EVASION
RATE
.
62
7.3
UAP
SEARCH
.
62
7.4
EVALUATION
.
64
7.5
SUMMARY
.
65
PART
IV
FEATURE-SPACE
ATTACKS
8
GRADIENT
OPTIMIZATION
.
69
8.1
CONVOLUTIONAL
NEURAL
NETWORKS
.
69
8.2
METHODOLOGY
.
70
8.3
SUMMARY
.
72
9
GENERATIVE
ADVERSARIAL
NETS
.
73
9.1
GANS
IN
SECURITY
.
74
9.2
METHODOLOGY
.
74
9.3
SUMMARY
.
76
PART
V
BENCHMARK
&
DEFENSES
10
COMPARISON
OF
STRATEGIES
.
79
10.1
BENCHMARK
SETTINGS
.
79
10.2
SUMMARY
.
81
11
TOWARDS
ROBUSTNESS
.
83
11.1
FEATURE
REDUCTION
.
84
11.2
UAP-BASED
ADVERSARIAL
TRAINING
.
85
11.2.1
EVALUATE
HARDENED
MODELS
.
87
11.2.2
UAP
SEARCH
ALTERNATIVE
.
89
11.3
SUMMARY
.
91
XXVI
CONTENTS
PART
VI
CLOSING
REMARKS
12
CONCLUSIONS
&
OUTLOOK
.
95
12.1
REVISING
RESEARCH
QUESTIONS
.
96
12.2
CONTRIBUTIONS
.
99
12.2.1
EMPIRICAL
GUIDANCE
FOR
MALWARE
ATTACKS
.
99
12.2.2
ADVERSARIAL
DEFENSES
DERIVED
FROM
UAP
ATTACKS
.
100
12.2.3
FRAMEWORK
TO
EVALUATE
MALWARE
CLASSIFIERS
.
100
12.3
CONNECTING
THE
DOTS
.
101
12.4
FUTURE
WORK
.
102
LIST
OF
PUBLICATIONS
.
105
REFERENCES
.
107 |
| any_adam_object | 1 |
| any_adam_object_boolean | 1 |
| author | Labaca-Castro, Raphael |
| author_GND | (DE-588)1290186871 |
| author_facet | Labaca-Castro, Raphael |
| author_role | aut |
| author_sort | Labaca-Castro, Raphael |
| author_variant | r l c rlc |
| building | Verbundindex |
| bvnumber | BV048968184 |
| classification_rvk | ST 300 |
| ctrlnum | (OCoLC)1379829788 (DE-599)DNB1274567319 |
| discipline | Informatik |
| discipline_str_mv | Informatik |
| format | Thesis Book |
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| genre | (DE-588)4113937-9 Hochschulschrift gnd-content |
| genre_facet | Hochschulschrift |
| id | DE-604.BV048968184 |
| illustrated | Illustrated |
| index_date | 2024-07-03T22:02:08Z |
| indexdate | 2024-07-10T09:51:32Z |
| institution | BVB |
| institution_GND | (DE-588)1043386068 |
| isbn | 9783658404413 3658404418 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-034231846 |
| oclc_num | 1379829788 |
| open_access_boolean | |
| owner | DE-706 DE-83 |
| owner_facet | DE-706 DE-83 |
| physical | xxxiv, 116 Seiten Illustrationen, Diagramme 21 cm x 14.8 cm, 209 g |
| publishDate | 2023 |
| publishDateSearch | 2023 |
| publishDateSort | 2023 |
| publisher | Springer Vieweg |
| record_format | marc |
| series2 | Research |
| spelling | Labaca-Castro, Raphael Verfasser (DE-588)1290186871 aut Machine learning under malware attack Raphael Labaca-Castro Wiesbaden Springer Vieweg [2023] © 2023 xxxiv, 116 Seiten Illustrationen, Diagramme 21 cm x 14.8 cm, 209 g txt rdacontent n rdamedia nc rdacarrier Research Dissertation Universität der Bundeswehr München 2022 Maschinelles Lernen (DE-588)4193754-5 gnd rswk-swf Computersicherheit (DE-588)4274324-2 gnd rswk-swf Malware (DE-588)4687059-3 gnd rswk-swf Computer Security Machine Learning Adversarial ML Trustworthy AI Malware FAME (DE-588)4113937-9 Hochschulschrift gnd-content Maschinelles Lernen (DE-588)4193754-5 s Malware (DE-588)4687059-3 s Computersicherheit (DE-588)4274324-2 s DE-604 Springer Fachmedien Wiesbaden (DE-588)1043386068 pbl Erscheint auch als Online-Ausgabe 978-3-658-40442-0 X:MVB text/html http://deposit.dnb.de/cgi-bin/dokserv?id=da01a9e477034c2c97a7683718d3b6f7&prov=M&dok_var=1&dok_ext=htm Inhaltstext B:DE-101 application/pdf https://d-nb.info/1274567319/04 Inhaltsverzeichnis DNB Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=034231846&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis 1\p vlb 20221203 DE-101 https://d-nb.info/provenance/plan#vlb |
| spellingShingle | Labaca-Castro, Raphael Machine learning under malware attack Maschinelles Lernen (DE-588)4193754-5 gnd Computersicherheit (DE-588)4274324-2 gnd Malware (DE-588)4687059-3 gnd |
| subject_GND | (DE-588)4193754-5 (DE-588)4274324-2 (DE-588)4687059-3 (DE-588)4113937-9 |
| title | Machine learning under malware attack |
| title_auth | Machine learning under malware attack |
| title_exact_search | Machine learning under malware attack |
| title_exact_search_txtP | Machine learning under malware attack |
| title_full | Machine learning under malware attack Raphael Labaca-Castro |
| title_fullStr | Machine learning under malware attack Raphael Labaca-Castro |
| title_full_unstemmed | Machine learning under malware attack Raphael Labaca-Castro |
| title_short | Machine learning under malware attack |
| title_sort | machine learning under malware attack |
| topic | Maschinelles Lernen (DE-588)4193754-5 gnd Computersicherheit (DE-588)4274324-2 gnd Malware (DE-588)4687059-3 gnd |
| topic_facet | Maschinelles Lernen Computersicherheit Malware Hochschulschrift |
| url | http://deposit.dnb.de/cgi-bin/dokserv?id=da01a9e477034c2c97a7683718d3b6f7&prov=M&dok_var=1&dok_ext=htm https://d-nb.info/1274567319/04 http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=034231846&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT labacacastroraphael machinelearningundermalwareattack AT springerfachmedienwiesbaden machinelearningundermalwareattack |